openaire-coar conference 2014: allowing research data to shine: providing tangible credit for data...

ALLOWING RESEARCH DATA TO SHINE: PROVIDING

TANGIBLE CREDIT FOR DATA SHARING

Joint OpenAIRE-COAR conference, 21-22nd May 2014

Varsha Khodiyar Editorial Biocurator, F1000Research

@vkf1000

f1000research.com @f1000research

http://f1000.com/

ABOUT FACULTY OF 1000

The Seer of Science Publishing Science 4 October 2013:

Vol. 342 no. 6154 pp. 66-67 DOI: 10.1126/science.342.6154.66

http://www.sciencemag.org/content/342/6154/66.full.pdf

@vkf1000 | @f1000research

WHAT IS F1000RESEARCH?

F1000Research is an open access journal for life scientists that

accepts all scientifically sound articles, ranging from single

findings, case reports, protocols, replications, and null or negative

results to more traditional articles.

Key features:

• Publication within a week

• Transparent, post-publication peer review

• All data included

• Accepts non-traditional article types


POST-PUBLICATION PEER REVIEW

Remove the publication delay.

Invited peer review (post-publication).

Transparent refereeing.

Inclusion of all data in all articles.

No restriction of access.

All article types published, including

data only papers.


WHY SHARE RESEARCH DATA?

WHY SHARING RESEARCH DATA IS IMPORTANT

Transparency and openness are cornerstones of the scientific method

“Not allowing reuse of

data is scientific

malpractice”

Royal Society; Science as

an open enterprise, Final

report 2012

http://royalsociety.org/about-us/history/


SHARING DATA ALLOWS REPLICATION

“[W]e evaluated the replication of data analyses in 18

articles on microarray-based gene expression profiling

published in Nature Genetics in 2005–2006...We

reproduced two analyses in principle and six partially or

with some discrepancies; ten could not be reproduced.

The main reason for failure to reproduce was data

unavailability.”

Ioannidis JPA. et al. Repeatability of published microarray

gene expression analyses. Nature Genetics 41, 149–55

(2009)


SHARING DATA CORRELATES WITH HIGHER CITATIONS

“We conclude there is a direct effect of third-party data

reuse that persists for years beyond the time when

researchers have published most of the papers reusing

their own data...We further conclude that...a

substantial fraction of archived datasets are reused,

and that the intensity of dataset reuse has been steadily

increasing since 2003.”

Piowar HA., Vision TA. Data reuse and the open data citation

advantage. PeerJ (2013) doi: 10.7717/peerj.175


SHARING DATA ADDITIONALLY PROMOTES

• Diversity of analyses and opinion

• Increased return on investment in research

• Reduction of error and fraud

• Education of new researchers

• New research

• meta-analyses to create new datasets

• testing of new hypotheses

• new analysis methods

• studies on data collection methods


PUSH TOWARDS SHARING RESEARCH DATA

http://www.biosharing.org/policies


TECHNICAL CONSIDERATIONS FOR SHARING

RESEARCH DATA

MAKING DATA ACCESSIBLE

‘Openly accessible’ – apply the principles of the Budapest Open Access

Initiative* (originally created for scholarly articles) to scholarly data

• Free to view/access

• Free to download

• Free to re-analyse (as a individual dataset or as part of a larger meta-

analysis)

• Free to modify

Community norms to be applied regarding acknowledgement and

citation of data.

* budapestopenaccessinitiative.org


DATA ACCESS AT F1000RESEARCH

• Free to view/access

• Free to download

- Available without a paywall

- Use open repositories to

host data

• Free to re-analyse and modify

- Use of CC0 licence

• Acknowledge data authors

- Ensure data is citable


MAKING DATA USABLE

• Present data in useable format (i.e. not a supplemental PDF!)

• Specify how data was generated

• Provide quality assurance (overview of limitations; peer

review)

• Share data in non-proprietary formats

• Specify access to software required to view data

• Specify parameters in software used to analyze the data


DATA USABILITY AT F1000RESEARCH


• Preview large datasets prior to

downloading

• View data without leaving the

article

• Usage statistics provided

• Legends and DOIs for data

• We encourage authors to

- Use non-proprietary formats, e.g. CSV

over XLS/XLSX

- Include detailed methods to allow

replication

• Quality assurance with

transparent peer review

F1000RESEARCH: DATA REVIEW

Internal pre-publication checks:

• Storage (discipline-specific repository where possible)

• Format

• Layout and labelling

• Adequate data?

• Adequate protocol information?

Referees are asked to check:

• Methods were appropriate?

• Format/structure usable?

• Data limitations and sources of error included?

• Adequate information to enable potential replication?

• Does the data ‘look’ OK?


DISCOVERING DATA AT F1000RESEARCH Strasser C, Kunze J, Abrams S, Cruse P (2014) DataUp: A tool to help researchers describe

and share tabular data [v1; ref status: awaiting peer review, http://f1000r.es/2n7]

F1000Research 2014, 3:6


HELPING RESEARCHERS TO OVERCOME CULTURAL

BARRIERS TO SHARING DATA

JOURNAL/PUBLISHER DATA SHARING POLICIES

• Reproducible research or data sharing statements in

published papers (Annals Internal Medicine, BMJ)

• Data sharing implied by submission (BioMed Central)

• Data sharing as a condition of publication (PLOS, NPG)

• As above AND data must be available to reviewers/editors

• Open data as a condition of submission (F1000Research)

• Papers rejected if data unavailable freely*

Policy strength increases


MAKING DATA SHARING EASY - IN-ARTICLE DATA MANIPULATION

A fixed-dose randomized

controlled trial of olanzapine

for psychosis in Parkinson

disease [v1; ref status: indexed,

http://f1000r.es/1au] Michelle J Nichols, Johanna M

Hartlein, Meredith GA Eicken, Brad A

Racette, Kevin J Black

F1000Research 2013, 2:150


FORMAL CREDIT FOR DATA SHARING

Feeding into currently recognized scholarly outputs

Benefits:

• Appropriate credit for data producers with a citable publication

• Data accessible from repository regardless of journal subscription

• Data independently discoverable via bi-directional linking

• Data available in usable form

• Potential increase in ‘value’ of data, as increasing numbers of

studies are carried out


FORMAL CREDIT FOR DATA SHARING

Novel measures of scholarly outputs

• Encourage data peer review, and promote scholarly credit for peer

review

• Record basic metrics for datasets

• Promote data sharing as a formal research output

• Participate in cross-publisher initiatives to ease data sharing

• Participate in discussions about research data feeding in to the

tenure process


DATA PUBLISHING AND SHARING IS NOT A NEW IDEA

“I have begun to think that no one ought to publish biometric results, without lodging a well-arranged and well-

bound manuscript copy of his data in some place where it should be accessible, under reasonable restrictions, to those

who desire to verify his work.”

Galton F. Biometry. Biometrika 1 (1): 7-10 (1901)

doi: 10.1093/biomet/1.1.7


Email: [email protected]

Twitter: @vkf1000 / @f1000research

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